Optimal Adaptive Designs for Binary Response Trials

We derive the optimal allocation between two treatments in a clinical trial based on the following optimality criterion: for fixed variance of the test statistic, what allocation minimizes the expected number of treatment failures? A sequential design is described that leads asymptotically to the op...

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Bibliographic Details
Published in:Biometrics 2001-09, Vol.57 (3), p.909-913
Main Authors: Rosenberger, William F., Stallard, Nigel, Ivanova, Anastasia, Harper, Cherice N., Ricks, Michelle L.
Format: Article
Language:English
Subjects:
Biometrics
Biometry
Clinical trials
Clinical Trials as Topic - statistics & numerical data
Error rates
Ethics
Ethics, Medical
Expected treatment failures
Humans
Lead
Medical treatment failures
Models, Statistical
Neyman allocation
Power
Probabilities
Proportions
Randomized Controlled Trials as Topic - statistics & numerical data
Randomized play-the-winner rule
Sample size
Sequential design
Statistical results
Statistical variance
Treatment Failure
variance
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Summary:We derive the optimal allocation between two treatments in a clinical trial based on the following optimality criterion: for fixed variance of the test statistic, what allocation minimizes the expected number of treatment failures? A sequential design is described that leads asymptotically to the optimal allocation and is compared with the randomized play‐the‐winner rule, sequential Neyman allocation, and equal allocation at similar power levels. We find that the sequential procedure generally results in fewer treatment failures than the other procedures, particularly when the success probabilities of treatments are smaller.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.0006-341X.2001.00909.x